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arXiv 2024年

作者： Yu, Zhihao Chu, Xu Jin, Yujie Wang, Yasha Zhao, Junfeng School of Computer Science Peking University China National Research and Engineering Center of Software Engineering Peking University China Center on Frontiers of Computing Studies Peking University Beijing China

Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status. However, the prevalence of missing data in EHR poses significant challenges to existing methods, leading to spurious correlations and suboptimal predictions. While various imputation techniques have been developed to address this issue, they often obsess unnecessary details and may introduce additional noise when making clinical predictions. To tackle this problem, we propose SMART, a Self-Supervised Missing-Aware RepresenTation Learning approach for patient health status prediction, which encodes missing information via elaborated attentions and learns to impute missing values through a novel self-supervised pre-training approach that reconstructs missing data representations in the latent space. By adopting missing-aware attentions and focusing on learning higher-order representations, SMART promotes better generalization and robustness to missing data. We validate the effectiveness of SMART through extensive experiments on six EHR tasks, demonstrating its superiority over state-of-the-art methods. © 2024, CC BY-NC-ND.

关键词： Forecasting

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Collaborative DNNs Inference with Joint Model Partition and Compression in Mobile Edge-Cloud computing Networks

Collaborative DNNs Inference with Joint Model Partition and ...

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IEEE Conference on Wireless Communications and Networking

作者： Yaxin Tang Xiuhua Li Hui Li Zhengyi Yang Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Departrnent of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Mobile edge-cloud computing utilizes the computing resources of edge devices and cloud servers to execute complex deep neural networks (DNNs) for collaborative inference. However, many existing collaborative inference methods do not fully consider the limited resources of edge devices, resulting in high inference latency. In this paper, we design an integrated computational framework that combines model partition and compression to reduce inference latency. Specifically, we partition a DNN model at the middle layer and deploy the previous layer on the edge device and the subsequent layer on the cloud server respectively. We propose a collaborative dual-agent reinforcement learning algorithm called CPCDRL to determine partition point and compression ratios. It enables adaptive adjustments of compression ratios based on various partition points, with the overarching goal of minimizing the inference latency across the entire DNN model. The proposed algorithm can significantly reduce computational latency while minimizing accuracy loss compared to the baseline schemes.

关键词： Performance evaluation Adaptation models Computational modeling Simulation Collaboration Artificial neural networks Reinforcement learning

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Multi-Agent Deep Reinforcement Learning for Computation Offloading in Multi-IRS Assisted Mobile Edge computing Networks

Multi-Agent Deep Reinforcement Learning for Computation Offl...

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IEEE Conference on Wireless Communications and Networking

作者： Lingxiao Chen Xiuhua Li Chuan Sun Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Mobile edge computing (MEC) as a potential technology can offload tasks from user devices (UDs) to network edges to alleviate network congestion and reduce task execution delay. However, computation offloading faces two challenges: 1) Poor wireless channel quality causes high transmission delay; 2) computing tasks may be obtained by eavesdroppers (Eves) during task offloading. Therefore, we consider deploying intel-ligent reflecting surface (IRS) in MEC networks to increase data transmission rate and ensure data transmission security. This paper investigates the issue of joint computation offloading and resource allocation in a multi-IRS assisted MEC network. Our goal is to minimize task execution delay. To address this problem, we propose a multi-agent deep deterministic policy gradient algorithm to determine the optimal offloading strategy for each UD. Simulation results show that the proposed algorithm can significantly reduce task execution delay and ensure data transmission security.

关键词： Wireless communication Multi-access edge computing Simulation Delays Data communication Security Resource management

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AKGIN: An API Knowledge Graph and Intent Network based Mashup-Oriented API Recommendation Method

AKGIN: An API Knowledge Graph and Intent Network based Mashu...

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International Conference on Computer Supported Cooperative Work in Design

作者： Changyong Yu Rong Hu Bangjun Wang Computing and Novel Software Technology HNUST School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

With the continuous development of the Web API ecosystem, mashup-oriented API recommendation gets a lot of attention. Collaborative filtering, deep learning and their combination based methods are recently proposed for API recommendation. However, the recommendation results often perform poorly when data is sparse. Therefore, we propose an API knowledge graph (AKG) and intent network based mashup-oriented API recommendation method, whose main target is to recruit the semantic representation ability to improve the precision of API recommendation. Firstly, Latent Dirichlet Allocation (LDA) is used to extract API topics, which are important entities in the AKG. Then, the mashup intention is modeled based on the mashup-API interactions. The high-order relationship paths are recursively aggregated into the representation vector of Mashup and API. Finally, the inner product of their representations present the probability of the API recommendation for the mashup. The experimental results on real-world datasets show that our proposed method outperforms several baseline methods in mashup-oriented API recommendations.

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Quantum and Classical Query Complexities for Determining Connectedness of Matroids

SSRN

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SSRN 2023年

作者： Huang, Xiaowei Feng, Shiguang Li, Lvzhou Institute of Quantum Computing and and Software School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China

Connectivity is a fundamental structural property of matroids, and has been studied algorithmically over 50 years. To the best of our knowledge, the classical query complexity lower bound and the quantum complexity for this problem have not been considered. Thus, in this paper we are devoted to addressing these issues, and our contributions are threefold as follows:(i) First, we prove that the randomized query complexity of determining whether a matroid is connected is $\Omega(n^2)$ and thus the algorithm proposed by Cunningham is optimal in classical computing. (ii) Second, we present a quantum algorithm with $O(n^{3/2})$ queries, which exhibits provable quantum speedups over classical ones.(iii) Third, we prove that any quantum algorithm requires $\Omega(n)$ queries, which indicates that quantum algorithms can achieve at most a quadratic speedup over classical ones. Therefore, we have a relatively comprehensive understanding of the potential of quantum computing in determining the connectedness of matroids. © 2023, The Authors. All rights reserved.

关键词： Quantum theory

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Extracting Common and Variable Code using the LCS Algorithm for Migration to SPLE

Extracting Common and Variable Code using the LCS Algorithm ...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Taeyoung Kim Jihyun Lee Sungwon Kang Department of Software Engineering Jeonbuk National University Jeonju Republic of Korea School of Computing KAIST Daejeon Republic of Korea

The LCS (Longest Common Subsequence) algorithm is a well-known algorithm that finds the longest subsequence from two different strings while preserving the relative order between the characters that make up the strings. When migrating from source code to software Product Line engineering (SPLE), the process of identifying commonality and variability is a crucial step and many studies have applied the LCS algorithm for this process, but this algorithm can be applied only to two sources although the cases of three or more sources are common. This study proposes a method that extracts common and variable code lines from three or more sources. The proposed method consists of the preprocessing phase that divides sources into sections by reflecting the characteristics of their programming languages, and the phase for applying the LCS algorithm. To evaluate the proposed method, we applied it to ArgoUML-SPL and compared the result with the original platform of ArgoUML-SPL, which confirmed that common and variable code lines were effectively identified.

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Predicting Modalities of Dyslexic Students using Neuro-Linguistic Programming to Enhance Learning Method

Predicting Modalities of Dyslexic Students using Neuro-Lingu...

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2022 IEEE International Conference on Data Science and Information System, ICDSIS 2022

作者： Choube, Gaurav Dudhmande, Gauri Rahul Pushparaj, Jagalingam Anand, Christopher Suresh, Shilpa Software Engineer Optum Global Solutions Pvt Ltd Hyderabad India Software Engineer Intel Technology Pvt Ltd Aurangabad India Vellore Institute of Technology School of Computer Science and Engineering Vellore India McMaster University Department of Computing and Software Ontario Canada Manipal Institute of Technology Department of Mechatronics Manipal India

ISBN: (数字)9781665498012

ISBN: (纸本)9781665498012

Dyslexia causes difficulty in reading, writing and learning. The children at a tender age have always suffered due to dyslexia. Dyslexia deceives student's perception and makes it difficult in the process of learning. In this paper, machine learning techniques like multi-layer perceptron, Decision tree and Gaussian NB approaches were implemented for the prediction of modalities. To enhance the learning approach for the students suffering from dyslexia, the predicted modalities can be adopted. The sampled data was trained, and the target labels were classified into three classes as visual, auditory, and kinesthetic. The data was processed and fed into multi-layer perceptron, decision tree and naive bayes machine learning algorithms using scikit-learn. Confusion matrix was used to evaluate the performance measure of the algorithms. It was observed that models achieved accuracy of 81.41% for MLP Classifier, 63.82% for Decision tree and 79.25% for Naive bayes. The best result was achieved by MLP Classifier. © 2022 IEEE.

关键词： Decision trees

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SPUFChain: Permissioned Blockchain Lightweight Authentication Scheme for Supply Chain Management Using PUF of IoT

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IEEE Access 2025年 13卷 88662-88682页

作者： Assaqty, Mohammad Iqbal Saryuddin Gao, Ying Alqarni, Abeer D. Khan, Siraj Chakraborty, Mohuya Nur Siddiqi, Hafied Ahmad, Sadique South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Universitas Nahdlatul Ulama Indonesia Faculty of Engineering and Computer Science Jakarta10320 Indonesia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Research Institute for Future Media Computing College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China University of Engineering and Management Cybersecurity Centre of Excellence Kolkata700160 India Riyadh12435 Saudi Arabia

The complexity of the entire process of supply chain management (SCM) is quite cumbersome and traditional way of handling it is devoid of proper authentication and security and very often suffers from human errors in dealing with flaws in quality control process of SCM. While it may have started with shipment tracking, the outcome of using IoT on supply chains has spread to every link in the chain. For instance, manufacturers are employing Internet-enabled sensors in production to find product faults, resulting in higher-quality production runs. Physical Unclonable Function (PUF) is a defense countermeasure that exploits the exclusive, unrepeatable features of hardware components to generate distinct cryptographic keys or identifiers, typically for a semiconductor device like an Internet of Things (IoT) device. The distinctive proof of identity of IoT devices in the supply chain is employed by means of PUFs as inviolable IDs. Blockchain, the distributed, immutable ledger, on the other hand is the disruptive technology that provides higher security as compared to traditional centralized systems. The integration of PUF and blockchain proves to be quite interesting while handling the above issues of authentication. A smart contract on the blockchain is a software code that executes spontaneously as and when the conditions of the contract or agreement are satisfied. Hence after authentication process the results are fed to blockchain smart contract for the final validation. This paper presents a novel permissioned blockchain smart contract-based lightweight authentication scheme for SCM using PUF of IoT known as SPUFChain. Informal and formal security analysis (using AVISPA and BAN logic) of the proposed framework show its efficiency to resist several threat scenarios like man-in-the middle, non-repudiation, impersonation, replay attacks and many other security features as compared to other related schemes. The processing time (~13.8ms) is better than existing lightweig

关键词： Supply chain management

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The Progression of Competencies and Dispositions: How do Maturing software Engineers Compare?

The Progression of Competencies and Dispositions: How do Mat...

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Frontiers in Education (FIE) Conference

作者： Tony Clear Alison Clear Department of Computer Science and Software Engineering Auckland University of Technology Auckand New Zealand School of Computing Eastern Institute of technology Auckland New Zealand

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research paper describes and extends the outcomes from an in- depth study investigating the difference in the expected skills requirements from junior software engineers to senior software engineers, and reflections on the findings from that study. It is a given that senior software engineers have more experience and skills than junior software engineers. However, a focus on their differing competencies and dispositions provides an enhanced mechanism for comparison. Gaps were identified in assessing “professional knowledge” as categorized by the IEEE/ACM computing Curriculum Overview Report (CC2020), and in assessing “dispositions”. It appeared that the specific scenario of comparing the expected competencies between junior and senior software engineers, tested the framework for assessing competencies developed in the CC2020 project and applied in its mapping to the IEEE/ACM Computer Science (CS2013) approved curriculum. In this study into the difference between Junior and Senior software Engineers, an initial review of relevant literature was conducted. The review found that research analyzing job requirements for software engineers of different levels was limited; “experience” as a keyword was seldom mentioned; and a common distinction was made between “soft” and “hard” skills - the latter being skills that were “technical”, such as programming languages, frameworks, libraries, and tools, whereas soft skills referred to skills such as personality traits, attitudes, and teamwork skills. In our extension of that work the notion of soft skills was unpacked into professional skills and dispositions. The process of mapping from the CC2020 competency framework to the CS2013 curriculum had deliberately modelled how to represent a competency-based rather than a knowledge-based curriculum. The critical deficiency identified here was the limitation imposed by adopting a skills framework based on the cognitive taxonomy, and thereby unwittingly omitting the crucial compa

关键词： Computer science Vocabulary Computer languages Reviews Taxonomy Knowledge based systems software Reflection Libraries Teamwork

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IntelliCare: Improving Healthcare Analysis with Variance-Controlled Patient-Level Knowledge from Large Language Models

arXiv

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arXiv 2024年

作者： Yu, Zhihao Jin, Yujie Xu, Yongxin Chu, Xu Wang, Yasha Zhao, Junfeng School of Computer Science Peking University China National Research and Engineering Center of Software Engineering Peking University China Center on Frontiers of Computing Studies Peking University Beijing China

While pioneering deep learning methods have made great strides in analyzing electronic health record (EHR) data, they often struggle to fully capture the semantics of diverse medical codes from limited data. The integration of external knowledge from Large Language Models (LLMs) presents a promising avenue for improving healthcare predictions. However, LLM analyses may exhibit significant variance due to ambiguity problems and inconsistency issues, hindering their effective utilization. To address these challenges, we propose IntelliCare, a novel framework that leverages LLMs to provide high-quality patient-level external knowledge and enhance existing EHR models. Concretely, IntelliCare identifies patient cohorts and employs task-relevant statistical information to augment LLM understanding and generation, effectively mitigating the ambiguity problem. Additionally, it refines LLM-derived knowledge through a hybrid approach, generating multiple analyses and calibrating them using both the EHR model and perplexity measures. Experimental evaluations on three clinical prediction tasks across two large-scale EHR datasets demonstrate that IntelliCare delivers significant performance improvements to existing methods, highlighting its potential in advancing personalized healthcare predictions and decision support systems. Our code is available at https://***/yzhHoward/IntelliCare. © 2024, CC BY-NC-ND.

关键词： Electronic health record

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